Combinations of Different FIR Windows for Removal of Baseline and Power Line Noise from Electrocardiogram
Bibliographic record
Abstract
Electrocardiogram (ECG) is a vital tool used for diagnosing various heart diseases. It is the graphical representation of the electrical activity of the heart. But the electrocardiographic signals are often corrupted by noise from diverse sources. The most significant noises that corrupt ECG signal are power line interference and baseline wanders. It is necessary to reduce the amount of these disturbances from ECG signal for proper identification and interpretation of heart condition. This paper investigates the performance of the different 'Band stop filter-High Pass filter' combinations of window based FIR filter for removing the baseline wander and power line noise present in electrocardiogram. The ECG signal is generated and then noises are added to the ECG signal using MATLAB® where filters are designed and analyzed using Filter Design and Analysis Tool (FDATool). 49 different 'Band stop filter-High Pass filter' combinations are made using seven different FIR windows namely Bartlett, Chebyshev, Hamming, Hann, Kaiser, Rectangular, Triangular. For filter order of 350 and 450, the performance of different window combinations are compared and analyzed in terms of Signal power, Peak-to-peak value, Signal to Noise Ratio (SNR) and Mean Square Error (MSE) of the filtered output. A further analysis of the waveforms of the filtered output show that the combinations where both the bandstop and highpass filters are either Kaiser or Rectangular window i.e. Kaiser-Kaiser, Kaiser-Rectangular, Rectangular-Kaiser and Rectangular-Rectangular windows give the best performance in reducing both the baseline noise and high frequency power line noise. It is also found that the reduction of baseline noise is better if 450 filter order is used instead of 350 order in the above mentioned best four combinations, although the amount of delay for 450 order is slightly higher.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".